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Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows

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arxiv 2405.20407 v3 pith:FO637YBY submitted 2024-05-30 physics.ins-det cs.LGhep-exhep-phphysics.data-an

Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows

Thorsten Buss , Frank Gaede , Gregor Kasieczka , Claudius Krause , David Shih This is my paper
classification physics.ins-det cs.LGhep-exhep-phphysics.data-an
keywords flowsnormalizingconvolutionall2lflowsmodelsshowersdatasetlayers
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In the quest to build generative surrogate models as computationally efficient alternatives to rule-based simulations, the quality of the generated samples remains a crucial frontier. So far, normalizing flows have been among the models with the best fidelity. However, as the latent space in such models is required to have the same dimensionality as the data space, scaling up normalizing flows to high dimensional datasets is not straightforward. The prior L2LFlows approach successfully used a series of separate normalizing flows and sequence of conditioning steps to circumvent this problem. In this work, we extend L2LFlows to simulate showers with a 9-times larger profile in the lateral direction. To achieve this, we introduce convolutional layers and U-Net-type connections, move from masked autoregressive flows to coupling layers, and demonstrate the successful modelling of showers in the ILD Electromagnetic Calorimeter as well as Dataset 3 from the public CaloChallenge dataset.

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Cited by 4 Pith papers

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    A one-step generative model for calorimeter showers, using MeanFlow, a learned Gaussian-mixture prior, and a physics-constrained loss, matches diffusion-model quality at far fewer evaluations.

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    hep-ex 2026-06 unverdicted novelty 5.0

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  4. Amplitude Uncertainties Everywhere All at Once

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    Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.